Typically, in computed tomography (CT) imaging systems, an x-ray source emits a fan-shaped or a cone-shaped x-ray beam toward a subject or object, such as a patient or a piece of luggage, positioned on a support. The beam, after being attenuated by the subject, impinges upon a detector assembly. The intensity of the attenuated x-ray beam received at the detector assembly is typically dependent upon the attenuation of the x-ray beam by the subject.
In known third generation CT systems, the x-ray source and the detector assembly are rotated on a rotatable gantry portion around the object to be imaged so that a gantry angle at which the fan-shaped x-ray beam intersects the object constantly changes. The detector assembly is typically made of a plurality of detector modules. Data representing the intensity of the received x-ray beam at each of the detector elements is collected across a range of gantry angles. The data are ultimately processed to form an image.
Conventional CT systems emit an x-ray with a polychromatic spectrum. The x-ray attenuation of each material in the subject depends on the energy of the emitted x-ray. Due to this relationship, images acquired with a polychromatic x-ray beam suffer from beam hardening artifacts. CT projection data that is acquired with a monochromatic x-ray beam does not suffer from beam hardening artifacts.
If CT projection data is acquired at multiple x-ray energy levels, it is possible to create images largely free from beam hardening artifacts that look like they were acquired with a monochromatic x-ray beam. Additionally, the CT projection data that is acquired at multiple x-ray energy levels contains additional information about the subject or object being imaged that is not contained within a conventional CT image.
Dual energy projection data can be used to reconstruct images using basis material decomposition (BMD) algorithms. The generated images are representative of a pair of selected basis material densities. An example basis pair would be water and iodine. Unlike conventional CT images that are expressed in Hounsfield units (HU), material density images are expressed in mass per unit volume.
In addition to material density images, dual energy projection data can be used to produce a new image with x-ray attenuation coefficients equivalent to a chosen monochromatic energy. Such a monochromatic image includes an image where the intensity values of the voxels are assigned as if a CT image were created by collecting projection data from the subject with a monochromatic x-ray beam.
Given a pair of material density images, it is possible to generate other basis material image pairs. For example, from a water and iodine image of the same anatomy, it is possible to generate a different pair of material density images such as calcium and gadolinium. Similarly, from a pair of basis material images, it is possible to generate a pair of monochromatic images, each at a specific energy. The inverse is also possible, i.e. from a pair of monochromatic images, a pair of basis material image pairs can be derived, or a pair of monochromatic images at different energies. Effective-Z images are a third type of image that can be derived from a pair of material density or monochromatic images. An effective-Z image displays the average atomic number of the material contained within a given image voxel.
While known systems and methods can be employed to create and display material density images, monochromatic images, and/or effective-Z images. Known systems and methods are not equipped to provide clinical insight into relationships among such images. Further, known systems and methods simply display images created using dual energy data, and are lacking in regard to user interaction and analysis.
Dual energy data is commonly procured, stored and accessed in healthcare environments. Healthcare environments, such as hospitals or clinics, include information systems, such as hospital information systems (HIS), radiology information systems (RIS), clinical information systems (CIS), and cardiovascular information systems (CVIS), and storage systems, such as picture archiving and communication systems (PACS), library information systems (LIS), and electronic medical records (EMR). Information stored may include patient medical histories, imaging data, test results, diagnosis information, management information, and/or scheduling information, for example. The information may be centrally stored or divided at a plurality of locations. Healthcare practitioners may desire to access patient information or other information at various points in a healthcare workflow. For example, during and/or after surgery, medical personnel may access patient information, such as images of a patient's anatomy, that are stored in a medical information system. Radiologists, cardiologists and/or other clinicians may review stored images and/or other information, for example.
Using a PACS and/or other workstation, a clinician, such as a radiologist or cardiologist, for example, may perform a variety of activities, such as an image reading, to facilitate a clinical workflow. A reading, such as a radiology or cardiology procedure reading, is a process of a healthcare practitioner, such as a radiologist or a cardiologist, viewing digital images of a patient. The practitioner performs a diagnosis based on a content of the diagnostic images and reports on results electronically (e.g., using dictation or otherwise) or on paper. The practitioner, such as a radiologist or cardiologist, typically uses other tools to perform diagnosis. Some examples of other tools are prior and related prior (historical) exams and their results, laboratory exams (such as blood work), allergies, pathology results, medication, alerts, document images, and other tools. For example, a radiologist or cardiologist typically looks into other systems such as laboratory information, electronic medical records, and healthcare information when reading examination results.
Improved systems and methods for analysis of multi-energy data are desirable, particularly in healthcare environments, where dual energy data, a type of multi-energy data, is commonly procured.